├── README.md
├── Missing Data.ipynb
├── 1. Mean Median Mode imputation.ipynb
├── 3. Capturing NAN values with a new feature.ipynb
├── 2. Random Sample Imputation.ipynb
└── Titanic.csv


/README.md:
--------------------------------------------------------------------------------
 1 | # Handling-Missing-Values
 2 | ![image](https://user-images.githubusercontent.com/61430438/102975994-ea7bfc80-4526-11eb-8bb1-dd5e36fcef4e.png)
 3 | 
 4 | ## Missing data, also known as missing values, is where some of the observations in a data set are blank.
 5 | ex. 
 6 | - At age feature womens hesitate to put down their age
 7 | - Men hesitate to show their salary
 8 | - informations are not that valid
 9 | 
10 | ### Type of data
11 | 
12 |     1. Categorical Data: 
13 |         It is a string type of data such as Gender, Sex, education, etc.
14 | 
15 |     2. Discrite Data:
16 |         It is a number type data which is whole number only such as How many bank account you have,How many bike you have, etc.
17 | 
18 |     3.Continous Data:
19 |         It is a number type data such as Age, Height, Profit, etc.
20 |     
21 | ### Different type of Missing data
22 | 
23 |     1. Missing Completely at Random (MCAR)
24 |         - The variable is missing completely at random (MCAR) if the probability of being missing is the same for all the observations.
25 |         - When data is MCAR, there is absolutely no relationship between the data missing and other values.
26 | 
27 |     2. Missing Data Not at Random (MNAR)
28 |         - There is absolutely some relationship between the data misssing and any other feature's  values in dataset. 
29 | 
30 |     3. Missing at Random(MAR)
31 |         - If the propensity for a data point to be missing is't related to the missing data, but it's related to some of the observed data.
32 |         - When Data is MAR, The data is missing but can be predicted from other information.
33 |     
34 | ## All techniques of handling Missing values
35 | 
36 |     1. Mean/Mode/Median replacement
37 |     2. Random sample imputation
38 |     3. Capturing NAN values with a new feature
39 |     4. End of Distribution imputation
40 |     5. Arbitrary imputation
41 |     6. Frequent categories imputation   
42 | #### Note we perform all this techniques on 'Titanic' dataset, you can download it from :
43 | - [Titanic Datset](https://github.com/Rushi21-kesh/Handling-Missing-Values/blob/main/Titanic.csv)
44 | 


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/Missing Data.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Missing data, also known as missing values, is where some of the observations in a data set are blank.\n",
  8 |     "ex. \n",
  9 |     "- At age feature womens hesitate to put down their age\n",
 10 |     "- Men hesitate to show their salary\n",
 11 |     "- informations are not that valid"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "markdown",
 16 |    "metadata": {},
 17 |    "source": [
 18 |     "### Different types of data"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "markdown",
 23 |    "metadata": {},
 24 |    "source": [
 25 |     "##### 1. Categorical Data:\n",
 26 |     "\n",
 27 |     "    It is a string type of data such as Gender, Sex, education, etc.\n",
 28 |     "        ex : \n",
 29 |     "                 Sex\n",
 30 |     "            0    male\n",
 31 |     "            1  female\n",
 32 |     "            2  female\n",
 33 |     "            3  female\n",
 34 |     "            4    male\n"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "markdown",
 39 |    "metadata": {},
 40 |    "source": [
 41 |     "#####  2. Discrite Data:\n",
 42 |     "\n",
 43 |     "    It is a number type data which is whole number only, such as How many bank account you have,How many bike you have, etc.\n",
 44 |     "    \n",
 45 |     "        ex:\n",
 46 |     "    \n",
 47 |     "                Id\tNo. of Bank account\n",
 48 |     "            0\t1      \t 2\n",
 49 |     "            1\t2       \t1\n",
 50 |     "            2\t3\t       3\n",
 51 |     "            3\t4       \t1\n",
 52 |     "            4\t5       \t2"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "markdown",
 57 |    "metadata": {},
 58 |    "source": [
 59 |     "##### 3. Continous Data:\n",
 60 |     "    \n",
 61 |     "    It is a number type data such as Age, Height, Profit, etc.\n",
 62 |     "\n",
 63 |     "        ex : \n",
 64 |     "                 Age\t Height\n",
 65 |     "            0\t22.0\t70.250\n",
 66 |     "            1\t38.0\t71.283\n",
 67 |     "            2\t26.0\t72.925\n",
 68 |     "            3\t35.0\t69.100\n",
 69 |     "            4\t35.0\t78.500\n"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "markdown",
 74 |    "metadata": {},
 75 |    "source": [
 76 |     "### Different types of Missing data"
 77 |    ]
 78 |   },
 79 |   {
 80 |    "cell_type": "markdown",
 81 |    "metadata": {},
 82 |    "source": [
 83 |     "##### 1. Missing Completely at Random (MCAR)\n",
 84 |     "    - The variable is missing completely at random (MCAR) if the probability of being missing is the same for all the observations.\n",
 85 |     "    - When data is MCAR, there is absolutely no relationship between the data missing and other values.\n",
 86 |     "    \n",
 87 |     "    ex. \n",
 88 |     "    \n",
 89 |     "\n",
 90 |     "        PassengerId\tSurvived\tPclass\tSex\tAge\tSibSp\tParch\tTicket\tFare\tCabin\tEmbarked\n",
 91 |     "        62\t           1\t     1\t\tfemale 38.0\t 0\t    0\t   113572\t80.0\t B28\t  NaN\n",
 92 |     "        830              1\t     1\t\tfemale 62.0     0\t  0         113572\t80.0    B28\t  NaN\n",
 93 |     "        \n",
 94 |     "     Null values present in Embarked feature there is no relationship between Embarked's Null values and any other features."
 95 |    ]
 96 |   },
 97 |   {
 98 |    "cell_type": "markdown",
 99 |    "metadata": {},
100 |    "source": [
101 |     "##### 2. Missing Data Not at Random (MNAR)\n",
102 |     "    - There is absolutely some relationship between the data misssing and any other feature's  values in dataset.\n",
103 |     "    \n",
104 |     "     ex: \n",
105 |     "        \n",
106 |     "            PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch  Ticket  Fare  Cabin  Embarked\n",
107 |     "            \t6\t       0\t    3\tmale\tNaN\t0\t0\t330877 8.4583  NaN\tQ\n",
108 |     "            \t18\t      1        2\tmale\tNaN\t0\t0\t244373 13.0000  NaN\tS\n",
109 |     "            \t20          1    \t3\tfemale  NaN\t0\t0\t2649   7.2250  NaN\tC\n",
110 |     "            \t27\t      0    \t3\tmale\tNaN\t0\t0    2631  7.2250\tNaN\tC\n",
111 |     "            \t29\t      1    \t3\tfemale NaN\t 0\t0   330959\t7.8792 NaN\tQ   \n",
112 |     "                \n",
113 |     "      Null values are present in 'Age' and 'Cabin' feature too so it is having relationship between this feature. Suppose person in that specific cabin is not survied and that's why their Age is unknown."
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "markdown",
118 |    "metadata": {},
119 |    "source": [
120 |     "##### 3. Missing at Random(MAR)\n",
121 |     "    - If the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data.\n",
122 |     "    - When Data is MAR, The data is missing but can be predicted from other information.\n",
123 |     "    \n",
124 |     "    ex:\n",
125 |     "        \n",
126 |     "              Sex\tAge\n",
127 |     "        \tfemale\tNaN\n",
128 |     "        \tmale  \t30\n",
129 |     "        \tmale\t  19\n",
130 |     "        \tfemale\tNaN\n",
131 |     "        \tfemale\t4"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "markdown",
136 |    "metadata": {},
137 |    "source": [
138 |     "## All techniques of handling Missing values\n",
139 |     "\n",
140 |     "    1. Mean/Mode/Median replacement\n",
141 |     "    2. Random sample imputation\n",
142 |     "    3. Capturing NAN values with a new feature\n",
143 |     "    4. End of Distribution imputation\n",
144 |     "    5. Arbitrary imputation\n",
145 |     "    6. Frequent categories imputation   "
146 |    ]
147 |   },
148 |   {
149 |    "cell_type": "markdown",
150 |    "metadata": {},
151 |    "source": [
152 |     "##### Note we perform all this techniques on 'Titanic' dataset, you can download it from :\n",
153 |     "- "
154 |    ]
155 |   }
156 |  ],
157 |  "metadata": {
158 |   "kernelspec": {
159 |    "display_name": "Python 3",
160 |    "language": "python",
161 |    "name": "python3"
162 |   },
163 |   "language_info": {
164 |    "codemirror_mode": {
165 |     "name": "ipython",
166 |     "version": 3
167 |    },
168 |    "file_extension": ".py",
169 |    "mimetype": "text/x-python",
170 |    "name": "python",
171 |    "nbconvert_exporter": "python",
172 |    "pygments_lexer": "ipython3",
173 |    "version": "3.7.3"
174 |   }
175 |  },
176 |  "nbformat": 4,
177 |  "nbformat_minor": 2
178 | }
179 | 


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/1. Mean Median Mode imputation.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# 1. Mean Median Mode imputation\n",
  8 |     "\n",
  9 |     "    - This techinique is apply when Data is Missing Completely at Random(MCAR)\n",
 10 |     "    - Means there is absolutely no relationship between the data missing and other values."
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "markdown",
 15 |    "metadata": {},
 16 |    "source": [
 17 |     "- Import Required libraries"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": 93,
 23 |    "metadata": {},
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "import pandas as pd\n",
 27 |     "import numpy as np\n",
 28 |     "import seaborn as sns\n",
 29 |     "import plotly "
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "markdown",
 34 |    "metadata": {},
 35 |    "source": [
 36 |     "- load Dataset "
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": 94,
 42 |    "metadata": {},
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "data = pd.read_csv('Titanic.csv')\n",
 46 |     "df1 = data.copy()"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": 95,
 52 |    "metadata": {},
 53 |    "outputs": [
 54 |     {
 55 |      "data": {
 56 |       "text/html": [
 57 |        "<div>\n",
 58 |        "<style scoped>\n",
 59 |        "    .dataframe tbody tr th:only-of-type {\n",
 60 |        "        vertical-align: middle;\n",
 61 |        "    }\n",
 62 |        "\n",
 63 |        "    .dataframe tbody tr th {\n",
 64 |        "        vertical-align: top;\n",
 65 |        "    }\n",
 66 |        "\n",
 67 |        "    .dataframe thead th {\n",
 68 |        "        text-align: right;\n",
 69 |        "    }\n",
 70 |        "</style>\n",
 71 |        "<table border=\"1\" class=\"dataframe\">\n",
 72 |        "  <thead>\n",
 73 |        "    <tr style=\"text-align: right;\">\n",
 74 |        "      <th></th>\n",
 75 |        "      <th>PassengerId</th>\n",
 76 |        "      <th>Survived</th>\n",
 77 |        "      <th>Pclass</th>\n",
 78 |        "      <th>Name</th>\n",
 79 |        "      <th>Sex</th>\n",
 80 |        "      <th>Age</th>\n",
 81 |        "      <th>SibSp</th>\n",
 82 |        "      <th>Parch</th>\n",
 83 |        "      <th>Ticket</th>\n",
 84 |        "      <th>Fare</th>\n",
 85 |        "      <th>Cabin</th>\n",
 86 |        "      <th>Embarked</th>\n",
 87 |        "    </tr>\n",
 88 |        "  </thead>\n",
 89 |        "  <tbody>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>0</th>\n",
 92 |        "      <td>1</td>\n",
 93 |        "      <td>0</td>\n",
 94 |        "      <td>3</td>\n",
 95 |        "      <td>Braund, Mr. Owen Harris</td>\n",
 96 |        "      <td>male</td>\n",
 97 |        "      <td>22.0</td>\n",
 98 |        "      <td>1</td>\n",
 99 |        "      <td>0</td>\n",
100 |        "      <td>A/5 21171</td>\n",
101 |        "      <td>7.2500</td>\n",
102 |        "      <td>NaN</td>\n",
103 |        "      <td>S</td>\n",
104 |        "    </tr>\n",
105 |        "    <tr>\n",
106 |        "      <th>1</th>\n",
107 |        "      <td>2</td>\n",
108 |        "      <td>1</td>\n",
109 |        "      <td>1</td>\n",
110 |        "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
111 |        "      <td>female</td>\n",
112 |        "      <td>38.0</td>\n",
113 |        "      <td>1</td>\n",
114 |        "      <td>0</td>\n",
115 |        "      <td>PC 17599</td>\n",
116 |        "      <td>71.2833</td>\n",
117 |        "      <td>C85</td>\n",
118 |        "      <td>C</td>\n",
119 |        "    </tr>\n",
120 |        "    <tr>\n",
121 |        "      <th>2</th>\n",
122 |        "      <td>3</td>\n",
123 |        "      <td>1</td>\n",
124 |        "      <td>3</td>\n",
125 |        "      <td>Heikkinen, Miss. Laina</td>\n",
126 |        "      <td>female</td>\n",
127 |        "      <td>26.0</td>\n",
128 |        "      <td>0</td>\n",
129 |        "      <td>0</td>\n",
130 |        "      <td>STON/O2. 3101282</td>\n",
131 |        "      <td>7.9250</td>\n",
132 |        "      <td>NaN</td>\n",
133 |        "      <td>S</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>3</th>\n",
137 |        "      <td>4</td>\n",
138 |        "      <td>1</td>\n",
139 |        "      <td>1</td>\n",
140 |        "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
141 |        "      <td>female</td>\n",
142 |        "      <td>35.0</td>\n",
143 |        "      <td>1</td>\n",
144 |        "      <td>0</td>\n",
145 |        "      <td>113803</td>\n",
146 |        "      <td>53.1000</td>\n",
147 |        "      <td>C123</td>\n",
148 |        "      <td>S</td>\n",
149 |        "    </tr>\n",
150 |        "    <tr>\n",
151 |        "      <th>4</th>\n",
152 |        "      <td>5</td>\n",
153 |        "      <td>0</td>\n",
154 |        "      <td>3</td>\n",
155 |        "      <td>Allen, Mr. William Henry</td>\n",
156 |        "      <td>male</td>\n",
157 |        "      <td>35.0</td>\n",
158 |        "      <td>0</td>\n",
159 |        "      <td>0</td>\n",
160 |        "      <td>373450</td>\n",
161 |        "      <td>8.0500</td>\n",
162 |        "      <td>NaN</td>\n",
163 |        "      <td>S</td>\n",
164 |        "    </tr>\n",
165 |        "  </tbody>\n",
166 |        "</table>\n",
167 |        "</div>"
168 |       ],
169 |       "text/plain": [
170 |        "   PassengerId  Survived  Pclass  \\\n",
171 |        "0            1         0       3   \n",
172 |        "1            2         1       1   \n",
173 |        "2            3         1       3   \n",
174 |        "3            4         1       1   \n",
175 |        "4            5         0       3   \n",
176 |        "\n",
177 |        "                                                Name     Sex   Age  SibSp  \\\n",
178 |        "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
179 |        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
180 |        "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
181 |        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
182 |        "4                           Allen, Mr. William Henry    male  35.0      0   \n",
183 |        "\n",
184 |        "   Parch            Ticket     Fare Cabin Embarked  \n",
185 |        "0      0         A/5 21171   7.2500   NaN        S  \n",
186 |        "1      0          PC 17599  71.2833   C85        C  \n",
187 |        "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
188 |        "3      0            113803  53.1000  C123        S  \n",
189 |        "4      0            373450   8.0500   NaN        S  "
190 |       ]
191 |      },
192 |      "execution_count": 95,
193 |      "metadata": {},
194 |      "output_type": "execute_result"
195 |     }
196 |    ],
197 |    "source": [
198 |     "df1.head()"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "markdown",
203 |    "metadata": {},
204 |    "source": [
205 |     "- Check null values"
206 |    ]
207 |   },
208 |   {
209 |    "cell_type": "code",
210 |    "execution_count": 96,
211 |    "metadata": {},
212 |    "outputs": [
213 |     {
214 |      "data": {
215 |       "text/plain": [
216 |        "PassengerId      0\n",
217 |        "Survived         0\n",
218 |        "Pclass           0\n",
219 |        "Name             0\n",
220 |        "Sex              0\n",
221 |        "Age            177\n",
222 |        "SibSp            0\n",
223 |        "Parch            0\n",
224 |        "Ticket           0\n",
225 |        "Fare             0\n",
226 |        "Cabin          687\n",
227 |        "Embarked         2\n",
228 |        "dtype: int64"
229 |       ]
230 |      },
231 |      "execution_count": 96,
232 |      "metadata": {},
233 |      "output_type": "execute_result"
234 |     }
235 |    ],
236 |    "source": [
237 |     "df1.isnull().sum()"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "markdown",
242 |    "metadata": {},
243 |    "source": [
244 |     "We can see that there is 177 null values in 'Age',687 in 'Cabin' and 2 in 'Embarked' feature."
245 |    ]
246 |   },
247 |   {
248 |    "cell_type": "markdown",
249 |    "metadata": {},
250 |    "source": [
251 |     "###  Handle Missing values by using Mean Median Mode imputation"
252 |    ]
253 |   },
254 |   {
255 |    "cell_type": "markdown",
256 |    "metadata": {},
257 |    "source": [
258 |     "- 1. Replace All NaN values in Age feature with mean"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "code",
263 |    "execution_count": 97,
264 |    "metadata": {},
265 |    "outputs": [
266 |     {
267 |      "data": {
268 |       "text/plain": [
269 |        "29.69911764705882"
270 |       ]
271 |      },
272 |      "execution_count": 97,
273 |      "metadata": {},
274 |      "output_type": "execute_result"
275 |     }
276 |    ],
277 |    "source": [
278 |     "# Calculate Mean\n",
279 |     "mean = df1['Age'].mean()\n",
280 |     "mean"
281 |    ]
282 |   },
283 |   {
284 |    "cell_type": "code",
285 |    "execution_count": 98,
286 |    "metadata": {},
287 |    "outputs": [],
288 |    "source": [
289 |     "# Method 1:\n",
290 |     "# df1['Age']=df1['Age'].fillna(mean) "
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 99,
296 |    "metadata": {},
297 |    "outputs": [],
298 |    "source": [
299 |     "# Method 2:\n",
300 |     "def fill_NAN_Age_mean(df1,variable,mean):\n",
301 |     "    df1[variable]=df1[variable].fillna(mean)"
302 |    ]
303 |   },
304 |   {
305 |    "cell_type": "code",
306 |    "execution_count": 100,
307 |    "metadata": {},
308 |    "outputs": [],
309 |    "source": [
310 |     "fill_NAN_Age_mean(df1,'Age',mean)"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "markdown",
315 |    "metadata": {},
316 |    "source": [
317 |     "- Check null values after replace NAN with mean "
318 |    ]
319 |   },
320 |   {
321 |    "cell_type": "code",
322 |    "execution_count": 101,
323 |    "metadata": {},
324 |    "outputs": [
325 |     {
326 |      "data": {
327 |       "text/plain": [
328 |        "0"
329 |       ]
330 |      },
331 |      "execution_count": 101,
332 |      "metadata": {},
333 |      "output_type": "execute_result"
334 |     }
335 |    ],
336 |    "source": [
337 |     "df1['Age'].isnull().sum()"
338 |    ]
339 |   },
340 |   {
341 |    "cell_type": "markdown",
342 |    "metadata": {},
343 |    "source": [
344 |     "## -------------------------------------------------------------------------------------------------------------------------------"
345 |    ]
346 |   },
347 |   {
348 |    "cell_type": "markdown",
349 |    "metadata": {},
350 |    "source": [
351 |     "### Now, Understand maybe there is some outlier are present in Age feature, at that time if we replace NaN with Mean then this feature not follow Gaussion Distribution.\n",
352 |     "\n",
353 |     "    - So insted of Mean use Median and replace NaN with it"
354 |    ]
355 |   },
356 |   {
357 |    "cell_type": "markdown",
358 |    "metadata": {},
359 |    "source": [
360 |     "- 2. Replace All NaN values in Age feature with median"
361 |    ]
362 |   },
363 |   {
364 |    "cell_type": "code",
365 |    "execution_count": 102,
366 |    "metadata": {},
367 |    "outputs": [],
368 |    "source": [
369 |     "df2 = data.copy()"
370 |    ]
371 |   },
372 |   {
373 |    "cell_type": "code",
374 |    "execution_count": 103,
375 |    "metadata": {},
376 |    "outputs": [
377 |     {
378 |      "data": {
379 |       "text/plain": [
380 |        "28.0"
381 |       ]
382 |      },
383 |      "execution_count": 103,
384 |      "metadata": {},
385 |      "output_type": "execute_result"
386 |     }
387 |    ],
388 |    "source": [
389 |     "#Calculate Median\n",
390 |     "median = df2['Age'].median()\n",
391 |     "median"
392 |    ]
393 |   },
394 |   {
395 |    "cell_type": "code",
396 |    "execution_count": 104,
397 |    "metadata": {},
398 |    "outputs": [],
399 |    "source": [
400 |     "# Method 1:\n",
401 |     "# df2['Age'] = df2['Age'].fillna(df['Age'].median())"
402 |    ]
403 |   },
404 |   {
405 |    "cell_type": "code",
406 |    "execution_count": 105,
407 |    "metadata": {},
408 |    "outputs": [],
409 |    "source": [
410 |     "#Method 2 :\n",
411 |     "def fill_NaN_Age_Median(df2,variable,median):\n",
412 |     "    df2[variable]=df2[variable].fillna(median)"
413 |    ]
414 |   },
415 |   {
416 |    "cell_type": "code",
417 |    "execution_count": 106,
418 |    "metadata": {},
419 |    "outputs": [],
420 |    "source": [
421 |     "fill_NaN_Age_Median(df2,'Age',median)"
422 |    ]
423 |   },
424 |   {
425 |    "cell_type": "code",
426 |    "execution_count": 107,
427 |    "metadata": {},
428 |    "outputs": [
429 |     {
430 |      "data": {
431 |       "text/plain": [
432 |        "0"
433 |       ]
434 |      },
435 |      "execution_count": 107,
436 |      "metadata": {},
437 |      "output_type": "execute_result"
438 |     }
439 |    ],
440 |    "source": [
441 |     "df2['Age'].isnull().sum()"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "markdown",
446 |    "metadata": {},
447 |    "source": [
448 |     "## -------------------------------------------------------------------------------------------------------------------------------"
449 |    ]
450 |   },
451 |   {
452 |    "cell_type": "markdown",
453 |    "metadata": {},
454 |    "source": [
455 |     "### If there is some frequent value are present in feature then replace NaN values with that frequent value.\n",
456 |     "ex. \n",
457 |     "    In classs 12th most of student is 17 years old, suppose we have some entries with Missing Age then we assume Age of that student is also 17 years and replace nan with 17 (we assume 17 is a frequent value)"
458 |    ]
459 |   },
460 |   {
461 |    "cell_type": "markdown",
462 |    "metadata": {},
463 |    "source": [
464 |     "- 3. Replace All NaN values in Age feature with mode"
465 |    ]
466 |   },
467 |   {
468 |    "cell_type": "code",
469 |    "execution_count": 108,
470 |    "metadata": {},
471 |    "outputs": [],
472 |    "source": [
473 |     "df3 = data.copy()"
474 |    ]
475 |   },
476 |   {
477 |    "cell_type": "code",
478 |    "execution_count": 109,
479 |    "metadata": {},
480 |    "outputs": [
481 |     {
482 |      "data": {
483 |       "text/plain": [
484 |        "24.0"
485 |       ]
486 |      },
487 |      "execution_count": 109,
488 |      "metadata": {},
489 |      "output_type": "execute_result"
490 |     }
491 |    ],
492 |    "source": [
493 |     "#Calculate mode\n",
494 |     "mode=df3['Age'].mode()[0]\n",
495 |     "mode"
496 |    ]
497 |   },
498 |   {
499 |    "cell_type": "code",
500 |    "execution_count": 110,
501 |    "metadata": {},
502 |    "outputs": [],
503 |    "source": [
504 |     "# method 1:\n",
505 |     "#df3['Age'] = df3['Age'].fillna(mode)"
506 |    ]
507 |   },
508 |   {
509 |    "cell_type": "code",
510 |    "execution_count": 111,
511 |    "metadata": {},
512 |    "outputs": [],
513 |    "source": [
514 |     "#method 2:\n",
515 |     "def fill_NAN_Age_mode(df3,variable,mode):\n",
516 |     "    df3[variable] = df3[variable].fillna(mode)"
517 |    ]
518 |   },
519 |   {
520 |    "cell_type": "code",
521 |    "execution_count": 112,
522 |    "metadata": {},
523 |    "outputs": [],
524 |    "source": [
525 |     "fill_NAN_Age_mode(df3,'Age',mode)"
526 |    ]
527 |   },
528 |   {
529 |    "cell_type": "code",
530 |    "execution_count": 113,
531 |    "metadata": {},
532 |    "outputs": [
533 |     {
534 |      "data": {
535 |       "text/plain": [
536 |        "0"
537 |       ]
538 |      },
539 |      "execution_count": 113,
540 |      "metadata": {},
541 |      "output_type": "execute_result"
542 |     }
543 |    ],
544 |    "source": [
545 |     "df3['Age'].isnull().sum()"
546 |    ]
547 |   },
548 |   {
549 |    "cell_type": "markdown",
550 |    "metadata": {},
551 |    "source": [
552 |     "## ------------------------------------------------- End ------------------------------------------------------------"
553 |    ]
554 |   }
555 |  ],
556 |  "metadata": {
557 |   "kernelspec": {
558 |    "display_name": "Python 3",
559 |    "language": "python",
560 |    "name": "python3"
561 |   },
562 |   "language_info": {
563 |    "codemirror_mode": {
564 |     "name": "ipython",
565 |     "version": 3
566 |    },
567 |    "file_extension": ".py",
568 |    "mimetype": "text/x-python",
569 |    "name": "python",
570 |    "nbconvert_exporter": "python",
571 |    "pygments_lexer": "ipython3",
572 |    "version": "3.8.5"
573 |   }
574 |  },
575 |  "nbformat": 4,
576 |  "nbformat_minor": 4
577 | }
578 | 


--------------------------------------------------------------------------------
/3. Capturing NAN values with a new feature.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Capturing NaN values with a new feature\n",
  8 |     "\n",
  9 |     "- In this method we create a new feature for NaN values where we set NaN=1 else 0\n",
 10 |     "- This method shoud apply when missing data type is MNAR\n",
 11 |     "- There is absolutely some relationship between the data misssing and any other feature's  values in dataset.\n",
 12 |     "- After creating mew feature then handle NaN values in old feature by using suitable method"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "markdown",
 17 |    "metadata": {},
 18 |    "source": [
 19 |     "### Import Libraries"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 1,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "import pandas as pd\n",
 29 |     "import numpy as np\n",
 30 |     "import seaborn as sns\n",
 31 |     "import matplotlib.pyplot as plt\n",
 32 |     "import missingno as mn"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "markdown",
 37 |    "metadata": {},
 38 |    "source": [
 39 |     "### Load Dataset"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": 2,
 45 |    "metadata": {},
 46 |    "outputs": [
 47 |     {
 48 |      "data": {
 49 |       "text/html": [
 50 |        "<div>\n",
 51 |        "<style scoped>\n",
 52 |        "    .dataframe tbody tr th:only-of-type {\n",
 53 |        "        vertical-align: middle;\n",
 54 |        "    }\n",
 55 |        "\n",
 56 |        "    .dataframe tbody tr th {\n",
 57 |        "        vertical-align: top;\n",
 58 |        "    }\n",
 59 |        "\n",
 60 |        "    .dataframe thead th {\n",
 61 |        "        text-align: right;\n",
 62 |        "    }\n",
 63 |        "</style>\n",
 64 |        "<table border=\"1\" class=\"dataframe\">\n",
 65 |        "  <thead>\n",
 66 |        "    <tr style=\"text-align: right;\">\n",
 67 |        "      <th></th>\n",
 68 |        "      <th>Survived</th>\n",
 69 |        "      <th>Age</th>\n",
 70 |        "      <th>Fare</th>\n",
 71 |        "    </tr>\n",
 72 |        "  </thead>\n",
 73 |        "  <tbody>\n",
 74 |        "    <tr>\n",
 75 |        "      <th>0</th>\n",
 76 |        "      <td>0</td>\n",
 77 |        "      <td>22.0</td>\n",
 78 |        "      <td>7.2500</td>\n",
 79 |        "    </tr>\n",
 80 |        "    <tr>\n",
 81 |        "      <th>1</th>\n",
 82 |        "      <td>1</td>\n",
 83 |        "      <td>38.0</td>\n",
 84 |        "      <td>71.2833</td>\n",
 85 |        "    </tr>\n",
 86 |        "    <tr>\n",
 87 |        "      <th>2</th>\n",
 88 |        "      <td>1</td>\n",
 89 |        "      <td>26.0</td>\n",
 90 |        "      <td>7.9250</td>\n",
 91 |        "    </tr>\n",
 92 |        "    <tr>\n",
 93 |        "      <th>3</th>\n",
 94 |        "      <td>1</td>\n",
 95 |        "      <td>35.0</td>\n",
 96 |        "      <td>53.1000</td>\n",
 97 |        "    </tr>\n",
 98 |        "    <tr>\n",
 99 |        "      <th>4</th>\n",
100 |        "      <td>0</td>\n",
101 |        "      <td>35.0</td>\n",
102 |        "      <td>8.0500</td>\n",
103 |        "    </tr>\n",
104 |        "  </tbody>\n",
105 |        "</table>\n",
106 |        "</div>"
107 |       ],
108 |       "text/plain": [
109 |        "   Survived   Age     Fare\n",
110 |        "0         0  22.0   7.2500\n",
111 |        "1         1  38.0  71.2833\n",
112 |        "2         1  26.0   7.9250\n",
113 |        "3         1  35.0  53.1000\n",
114 |        "4         0  35.0   8.0500"
115 |       ]
116 |      },
117 |      "execution_count": 2,
118 |      "metadata": {},
119 |      "output_type": "execute_result"
120 |     }
121 |    ],
122 |    "source": [
123 |     "data = pd.read_csv('Titanic.csv',usecols=['Age','Fare','Survived'])\n",
124 |     "data.head()"
125 |    ]
126 |   },
127 |   {
128 |    "cell_type": "markdown",
129 |    "metadata": {},
130 |    "source": [
131 |     "### Check NaN values"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 5,
137 |    "metadata": {},
138 |    "outputs": [
139 |     {
140 |      "data": {
141 |       "text/plain": [
142 |        "Survived      0\n",
143 |        "Age         177\n",
144 |        "Fare          0\n",
145 |        "dtype: int64"
146 |       ]
147 |      },
148 |      "execution_count": 5,
149 |      "metadata": {},
150 |      "output_type": "execute_result"
151 |     }
152 |    ],
153 |    "source": [
154 |     "data.isnull().sum()"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "code",
159 |    "execution_count": 15,
160 |    "metadata": {},
161 |    "outputs": [
162 |     {
163 |      "data": {
164 |       "text/plain": [
165 |        "Survived    0.000000\n",
166 |        "Age         0.198653\n",
167 |        "Fare        0.000000\n",
168 |        "dtype: float64"
169 |       ]
170 |      },
171 |      "execution_count": 15,
172 |      "metadata": {},
173 |      "output_type": "execute_result"
174 |     }
175 |    ],
176 |    "source": [
177 |     "data.isnull().mean()"
178 |    ]
179 |   },
180 |   {
181 |    "cell_type": "code",
182 |    "execution_count": 14,
183 |    "metadata": {},
184 |    "outputs": [
185 |     {
186 |      "data": {
187 |       "text/plain": [
188 |        "<AxesSubplot:>"
189 |       ]
190 |      },
191 |      "execution_count": 14,
192 |      "metadata": {},
193 |      "output_type": "execute_result"
194 |     },
195 |     {
196 |      "data": {
197 |       "image/png": 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\n",
198 |       "text/plain": [
199 |        "<Figure size 1800x720 with 2 Axes>"
200 |       ]
201 |      },
202 |      "metadata": {
203 |       "needs_background": "light"
204 |      },
205 |      "output_type": "display_data"
206 |     }
207 |    ],
208 |    "source": [
209 |     "mn.matrix(data)"
210 |    ]
211 |   },
212 |   {
213 |    "cell_type": "markdown",
214 |    "metadata": {},
215 |    "source": [
216 |     "- There is 177 NaN values are present in Age feature it is approximately 20%"
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "markdown",
221 |    "metadata": {},
222 |    "source": [
223 |     "## Now, Apply Capturing NaN values with a new feature method"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": 18,
229 |    "metadata": {},
230 |    "outputs": [],
231 |    "source": [
232 |     "data['Age_NaN'] = np.where(data['Age'].isnull,0,1)"
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "markdown",
237 |    "metadata": {},
238 |    "source": [
239 |     "- data['Age_NaN'] this is our new feature\n",
240 |     "- Where data['Age'] feature is having NaN values there data['Age_NaN'] values is 1 else 0"
241 |    ]
242 |   },
243 |   {
244 |    "cell_type": "code",
245 |    "execution_count": 19,
246 |    "metadata": {},
247 |    "outputs": [
248 |     {
249 |      "data": {
250 |       "text/html": [
251 |        "<div>\n",
252 |        "<style scoped>\n",
253 |        "    .dataframe tbody tr th:only-of-type {\n",
254 |        "        vertical-align: middle;\n",
255 |        "    }\n",
256 |        "\n",
257 |        "    .dataframe tbody tr th {\n",
258 |        "        vertical-align: top;\n",
259 |        "    }\n",
260 |        "\n",
261 |        "    .dataframe thead th {\n",
262 |        "        text-align: right;\n",
263 |        "    }\n",
264 |        "</style>\n",
265 |        "<table border=\"1\" class=\"dataframe\">\n",
266 |        "  <thead>\n",
267 |        "    <tr style=\"text-align: right;\">\n",
268 |        "      <th></th>\n",
269 |        "      <th>Survived</th>\n",
270 |        "      <th>Age</th>\n",
271 |        "      <th>Fare</th>\n",
272 |        "      <th>Age_NaN</th>\n",
273 |        "    </tr>\n",
274 |        "  </thead>\n",
275 |        "  <tbody>\n",
276 |        "    <tr>\n",
277 |        "      <th>0</th>\n",
278 |        "      <td>0</td>\n",
279 |        "      <td>22.0</td>\n",
280 |        "      <td>7.2500</td>\n",
281 |        "      <td>0</td>\n",
282 |        "    </tr>\n",
283 |        "    <tr>\n",
284 |        "      <th>1</th>\n",
285 |        "      <td>1</td>\n",
286 |        "      <td>38.0</td>\n",
287 |        "      <td>71.2833</td>\n",
288 |        "      <td>0</td>\n",
289 |        "    </tr>\n",
290 |        "    <tr>\n",
291 |        "      <th>2</th>\n",
292 |        "      <td>1</td>\n",
293 |        "      <td>26.0</td>\n",
294 |        "      <td>7.9250</td>\n",
295 |        "      <td>0</td>\n",
296 |        "    </tr>\n",
297 |        "    <tr>\n",
298 |        "      <th>3</th>\n",
299 |        "      <td>1</td>\n",
300 |        "      <td>35.0</td>\n",
301 |        "      <td>53.1000</td>\n",
302 |        "      <td>0</td>\n",
303 |        "    </tr>\n",
304 |        "    <tr>\n",
305 |        "      <th>4</th>\n",
306 |        "      <td>0</td>\n",
307 |        "      <td>35.0</td>\n",
308 |        "      <td>8.0500</td>\n",
309 |        "      <td>0</td>\n",
310 |        "    </tr>\n",
311 |        "  </tbody>\n",
312 |        "</table>\n",
313 |        "</div>"
314 |       ],
315 |       "text/plain": [
316 |        "   Survived   Age     Fare  Age_NaN\n",
317 |        "0         0  22.0   7.2500        0\n",
318 |        "1         1  38.0  71.2833        0\n",
319 |        "2         1  26.0   7.9250        0\n",
320 |        "3         1  35.0  53.1000        0\n",
321 |        "4         0  35.0   8.0500        0"
322 |       ]
323 |      },
324 |      "execution_count": 19,
325 |      "metadata": {},
326 |      "output_type": "execute_result"
327 |     }
328 |    ],
329 |    "source": [
330 |     "data.head()"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "markdown",
335 |    "metadata": {},
336 |    "source": [
337 |     "- We created a new feature for capturing a NaN \n",
338 |     "- Now you can handle NaN in data['Age'] feature \n",
339 |     "-  For handling this data['Age'] feature you can use verious methods\n",
340 |     "\n",
341 |     "   ex:\n",
342 |     "    - mean mode median imputation\n",
343 |     "        - [Kaggle](https://www.kaggle.com/rushikeshlavate/mean-median-mode-imputation-on-titanic-dataset) \n",
344 |     "        - [GitHub](https://github.com/Rushi21-kesh/Handling-Missing-Values/blob/main/1.%20Mean%20Median%20Mode%20imputation.ipynb)\n",
345 |     "    - random sample imputation\n",
346 |     "        - [Kaggle](https://www.kaggle.com/rushikeshlavate/random-sample-imputation-on-titanic-dataset)\n",
347 |     "        - [GitHub](https://github.com/Rushi21-kesh/Handling-Missing-Values/blob/main/2.%20Random%20Sample%20Imputation.ipynb)"
348 |    ]
349 |   },
350 |   {
351 |    "cell_type": "markdown",
352 |    "metadata": {},
353 |    "source": [
354 |     "- We use 'capturing NaN values with a new feature' because after creating a new feature for NaN we replace NaN with other values. \n",
355 |     "- It helps to the model to understand there is something happened here and it captures the importance of NaN. \n",
356 |     "- This method is create additional features therfore it work better with less number of feature "
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "markdown",
361 |    "metadata": {},
362 |    "source": [
363 |     "### I hope this notebook will helps you 😇😇😇\n",
364 |     "## Thank you !"
365 |    ]
366 |   }
367 |  ],
368 |  "metadata": {
369 |   "kernelspec": {
370 |    "display_name": "Python 3",
371 |    "language": "python",
372 |    "name": "python3"
373 |   },
374 |   "language_info": {
375 |    "codemirror_mode": {
376 |     "name": "ipython",
377 |     "version": 3
378 |    },
379 |    "file_extension": ".py",
380 |    "mimetype": "text/x-python",
381 |    "name": "python",
382 |    "nbconvert_exporter": "python",
383 |    "pygments_lexer": "ipython3",
384 |    "version": "3.8.5"
385 |   }
386 |  },
387 |  "nbformat": 4,
388 |  "nbformat_minor": 4
389 | }
390 | 


--------------------------------------------------------------------------------
/2. Random Sample Imputation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Random Sample Imputation\n",
  8 |     "\n",
  9 |     "-  Random Sample Imputation take a random observation from the feature.\n",
 10 |     "-  After that we use random observation to replace NaN in that feature.\n",
 11 |     "-  It should be used when data is missing completely at  random (MCAR)"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "markdown",
 16 |    "metadata": {},
 17 |    "source": [
 18 |     "### Import Required Libraries "
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 1,
 24 |    "metadata": {},
 25 |    "outputs": [],
 26 |    "source": [
 27 |     "import pandas as pd\n",
 28 |     "import numpy as np\n",
 29 |     "import seaborn as sns\n",
 30 |     "import matplotlib.pyplot as plt\n",
 31 |     "%matplotlib inline"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "markdown",
 36 |    "metadata": {},
 37 |    "source": [
 38 |     "### Loading dataset"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 2,
 44 |    "metadata": {},
 45 |    "outputs": [
 46 |     {
 47 |      "data": {
 48 |       "text/html": [
 49 |        "<div>\n",
 50 |        "<style scoped>\n",
 51 |        "    .dataframe tbody tr th:only-of-type {\n",
 52 |        "        vertical-align: middle;\n",
 53 |        "    }\n",
 54 |        "\n",
 55 |        "    .dataframe tbody tr th {\n",
 56 |        "        vertical-align: top;\n",
 57 |        "    }\n",
 58 |        "\n",
 59 |        "    .dataframe thead th {\n",
 60 |        "        text-align: right;\n",
 61 |        "    }\n",
 62 |        "</style>\n",
 63 |        "<table border=\"1\" class=\"dataframe\">\n",
 64 |        "  <thead>\n",
 65 |        "    <tr style=\"text-align: right;\">\n",
 66 |        "      <th></th>\n",
 67 |        "      <th>Survived</th>\n",
 68 |        "      <th>Age</th>\n",
 69 |        "      <th>Fare</th>\n",
 70 |        "    </tr>\n",
 71 |        "  </thead>\n",
 72 |        "  <tbody>\n",
 73 |        "    <tr>\n",
 74 |        "      <th>0</th>\n",
 75 |        "      <td>0</td>\n",
 76 |        "      <td>22.0</td>\n",
 77 |        "      <td>7.2500</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>1</th>\n",
 81 |        "      <td>1</td>\n",
 82 |        "      <td>38.0</td>\n",
 83 |        "      <td>71.2833</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>2</th>\n",
 87 |        "      <td>1</td>\n",
 88 |        "      <td>26.0</td>\n",
 89 |        "      <td>7.9250</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <th>3</th>\n",
 93 |        "      <td>1</td>\n",
 94 |        "      <td>35.0</td>\n",
 95 |        "      <td>53.1000</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>4</th>\n",
 99 |        "      <td>0</td>\n",
100 |        "      <td>35.0</td>\n",
101 |        "      <td>8.0500</td>\n",
102 |        "    </tr>\n",
103 |        "  </tbody>\n",
104 |        "</table>\n",
105 |        "</div>"
106 |       ],
107 |       "text/plain": [
108 |        "   Survived   Age     Fare\n",
109 |        "0         0  22.0   7.2500\n",
110 |        "1         1  38.0  71.2833\n",
111 |        "2         1  26.0   7.9250\n",
112 |        "3         1  35.0  53.1000\n",
113 |        "4         0  35.0   8.0500"
114 |       ]
115 |      },
116 |      "execution_count": 2,
117 |      "metadata": {},
118 |      "output_type": "execute_result"
119 |     }
120 |    ],
121 |    "source": [
122 |     "df = pd.read_csv('Titanic.csv',usecols=['Age','Fare','Survived'])\n",
123 |     "df.head()"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "markdown",
128 |    "metadata": {},
129 |    "source": [
130 |     "### Check null values"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "code",
135 |    "execution_count": 3,
136 |    "metadata": {},
137 |    "outputs": [
138 |     {
139 |      "data": {
140 |       "text/plain": [
141 |        "Survived      0\n",
142 |        "Age         177\n",
143 |        "Fare          0\n",
144 |        "dtype: int64"
145 |       ]
146 |      },
147 |      "execution_count": 3,
148 |      "metadata": {},
149 |      "output_type": "execute_result"
150 |     }
151 |    ],
152 |    "source": [
153 |     "df.isnull().sum()"
154 |    ]
155 |   },
156 |   {
157 |    "cell_type": "markdown",
158 |    "metadata": {},
159 |    "source": [
160 |     "    - There is 177 null values are present in Age feature."
161 |    ]
162 |   },
163 |   {
164 |    "cell_type": "markdown",
165 |    "metadata": {},
166 |    "source": [
167 |     "##  Now, we replace this NaN by using Random Sample Imputation"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 11,
173 |    "metadata": {},
174 |    "outputs": [
175 |     {
176 |      "data": {
177 |       "text/plain": [
178 |        "137    37.0\n",
179 |        "Name: Age, dtype: float64"
180 |       ]
181 |      },
182 |      "execution_count": 11,
183 |      "metadata": {},
184 |      "output_type": "execute_result"
185 |     }
186 |    ],
187 |    "source": [
188 |     "df['Age'].dropna().sample()"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "markdown",
193 |    "metadata": {},
194 |    "source": [
195 |     "    - where .dropna() function drop all nan values in that feature and .sample() function return any one random value"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 23,
201 |    "metadata": {},
202 |    "outputs": [
203 |     {
204 |      "data": {
205 |       "text/plain": [
206 |        "37     21.0\n",
207 |        "323    22.0\n",
208 |        "492    55.0\n",
209 |        "377    27.0\n",
210 |        "855    18.0\n",
211 |        "       ... \n",
212 |        "646    19.0\n",
213 |        "332    38.0\n",
214 |        "376    22.0\n",
215 |        "44     19.0\n",
216 |        "699    42.0\n",
217 |        "Name: Age, Length: 177, dtype: float64"
218 |       ]
219 |      },
220 |      "execution_count": 23,
221 |      "metadata": {},
222 |      "output_type": "execute_result"
223 |     }
224 |    ],
225 |    "source": [
226 |     "df['Age'].dropna().sample(df['Age'].isnull().sum())"
227 |    ]
228 |   },
229 |   {
230 |    "cell_type": "markdown",
231 |    "metadata": {},
232 |    "source": [
233 |     "    - this function check where is null values are present and replace Nan with random sample"
234 |    ]
235 |   },
236 |   {
237 |    "cell_type": "code",
238 |    "execution_count": 24,
239 |    "metadata": {},
240 |    "outputs": [
241 |     {
242 |      "data": {
243 |       "text/plain": [
244 |        "423    28.00\n",
245 |        "177    50.00\n",
246 |        "305     0.92\n",
247 |        "292    36.00\n",
248 |        "889    26.00\n",
249 |        "       ...  \n",
250 |        "539    22.00\n",
251 |        "267    25.00\n",
252 |        "352    15.00\n",
253 |        "99     34.00\n",
254 |        "689    15.00\n",
255 |        "Name: Age, Length: 177, dtype: float64"
256 |       ]
257 |      },
258 |      "execution_count": 24,
259 |      "metadata": {},
260 |      "output_type": "execute_result"
261 |     }
262 |    ],
263 |    "source": [
264 |     "df['Age'].dropna().sample(df['Age'].isnull().sum(),random_state=0)"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "markdown",
269 |    "metadata": {},
270 |    "source": [
271 |     "    - This function check where is null values are present and replace Nan with random sample\n",
272 |     "    - We use random_state because it replace NaN with specific value only ( if we not use random_state then values change evrytime when we run .)"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 26,
278 |    "metadata": {},
279 |    "outputs": [
280 |     {
281 |      "data": {
282 |       "text/plain": [
283 |        "Int64Index([  5,  17,  19,  26,  28,  29,  31,  32,  36,  42,\n",
284 |        "            ...\n",
285 |        "            832, 837, 839, 846, 849, 859, 863, 868, 878, 888],\n",
286 |        "           dtype='int64', length=177)"
287 |       ]
288 |      },
289 |      "execution_count": 26,
290 |      "metadata": {},
291 |      "output_type": "execute_result"
292 |     }
293 |    ],
294 |    "source": [
295 |     "df[df['Age'].isnull()].index"
296 |    ]
297 |   },
298 |   {
299 |    "cell_type": "markdown",
300 |    "metadata": {},
301 |    "source": [
302 |     "    - This line of code return a index where Age is NaN"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "markdown",
307 |    "metadata": {},
308 |    "source": [
309 |     "### Create A function which Replace NaN with Random Sample\n",
310 |     "- Here we creating a two new features Age_median and Age_random \n",
311 |     "- In Age_median replace NaN with Median and In Age_random replce NaN with Random Sample "
312 |    ]
313 |   },
314 |   {
315 |    "cell_type": "code",
316 |    "execution_count": 28,
317 |    "metadata": {},
318 |    "outputs": [],
319 |    "source": [
320 |     "def RSI(df,variable,median):\n",
321 |     "    df[variable+'_median'] = df[variable].fillna(median)#replace Nan with median\n",
322 |     "    df[variable+'_random'] = df[variable]#Copy feature into new feature\n",
323 |     "    #calculate random smaple and store into random_sample_values\n",
324 |     "    random_sample_value = df[variable].dropna().sample(df[variable].isnull().sum(),random_state=0)\n",
325 |     "    #in random_sample_value all filled nan values are present now we want to put/merge this all filled values in our dataset\n",
326 |     "    # for this we want to match all nan values index in random_sample_values with df[variavle_'random] \n",
327 |     "    #Pandas need to have same index in order to merge dataset\n",
328 |     "    random_sample_value.index = df[df[variable].isnull()].index#find index of NaN values in feature\n",
329 |     "    #now put a condition where ever it is null with loc function then replace with random_sample_values\n",
330 |     "    df.loc[df[variable].isnull(),variable+'_random'] =random_sample_value"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "markdown",
335 |    "metadata": {},
336 |    "source": [
337 |     "- calculate median"
338 |    ]
339 |   },
340 |   {
341 |    "cell_type": "code",
342 |    "execution_count": 29,
343 |    "metadata": {},
344 |    "outputs": [],
345 |    "source": [
346 |     "median = df['Age'].median()"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "markdown",
351 |    "metadata": {},
352 |    "source": [
353 |     "- Call Function\n",
354 |     "    - RSI('dataframe','Feature','median')\n"
355 |    ]
356 |   },
357 |   {
358 |    "cell_type": "code",
359 |    "execution_count": 33,
360 |    "metadata": {},
361 |    "outputs": [],
362 |    "source": [
363 |     "RSI(df,'Age',median)"
364 |    ]
365 |   },
366 |   {
367 |    "cell_type": "code",
368 |    "execution_count": 31,
369 |    "metadata": {},
370 |    "outputs": [
371 |     {
372 |      "data": {
373 |       "text/html": [
374 |        "<div>\n",
375 |        "<style scoped>\n",
376 |        "    .dataframe tbody tr th:only-of-type {\n",
377 |        "        vertical-align: middle;\n",
378 |        "    }\n",
379 |        "\n",
380 |        "    .dataframe tbody tr th {\n",
381 |        "        vertical-align: top;\n",
382 |        "    }\n",
383 |        "\n",
384 |        "    .dataframe thead th {\n",
385 |        "        text-align: right;\n",
386 |        "    }\n",
387 |        "</style>\n",
388 |        "<table border=\"1\" class=\"dataframe\">\n",
389 |        "  <thead>\n",
390 |        "    <tr style=\"text-align: right;\">\n",
391 |        "      <th></th>\n",
392 |        "      <th>Survived</th>\n",
393 |        "      <th>Age</th>\n",
394 |        "      <th>Fare</th>\n",
395 |        "      <th>Age_median</th>\n",
396 |        "      <th>Age_random</th>\n",
397 |        "    </tr>\n",
398 |        "  </thead>\n",
399 |        "  <tbody>\n",
400 |        "    <tr>\n",
401 |        "      <th>0</th>\n",
402 |        "      <td>0</td>\n",
403 |        "      <td>22.0</td>\n",
404 |        "      <td>7.2500</td>\n",
405 |        "      <td>22.0</td>\n",
406 |        "      <td>22.0</td>\n",
407 |        "    </tr>\n",
408 |        "    <tr>\n",
409 |        "      <th>1</th>\n",
410 |        "      <td>1</td>\n",
411 |        "      <td>38.0</td>\n",
412 |        "      <td>71.2833</td>\n",
413 |        "      <td>38.0</td>\n",
414 |        "      <td>38.0</td>\n",
415 |        "    </tr>\n",
416 |        "    <tr>\n",
417 |        "      <th>2</th>\n",
418 |        "      <td>1</td>\n",
419 |        "      <td>26.0</td>\n",
420 |        "      <td>7.9250</td>\n",
421 |        "      <td>26.0</td>\n",
422 |        "      <td>26.0</td>\n",
423 |        "    </tr>\n",
424 |        "    <tr>\n",
425 |        "      <th>3</th>\n",
426 |        "      <td>1</td>\n",
427 |        "      <td>35.0</td>\n",
428 |        "      <td>53.1000</td>\n",
429 |        "      <td>35.0</td>\n",
430 |        "      <td>35.0</td>\n",
431 |        "    </tr>\n",
432 |        "    <tr>\n",
433 |        "      <th>4</th>\n",
434 |        "      <td>0</td>\n",
435 |        "      <td>35.0</td>\n",
436 |        "      <td>8.0500</td>\n",
437 |        "      <td>35.0</td>\n",
438 |        "      <td>35.0</td>\n",
439 |        "    </tr>\n",
440 |        "  </tbody>\n",
441 |        "</table>\n",
442 |        "</div>"
443 |       ],
444 |       "text/plain": [
445 |        "   Survived   Age     Fare  Age_median  Age_random\n",
446 |        "0         0  22.0   7.2500        22.0        22.0\n",
447 |        "1         1  38.0  71.2833        38.0        38.0\n",
448 |        "2         1  26.0   7.9250        26.0        26.0\n",
449 |        "3         1  35.0  53.1000        35.0        35.0\n",
450 |        "4         0  35.0   8.0500        35.0        35.0"
451 |       ]
452 |      },
453 |      "execution_count": 31,
454 |      "metadata": {},
455 |      "output_type": "execute_result"
456 |     }
457 |    ],
458 |    "source": [
459 |     "df.head()"
460 |    ]
461 |   },
462 |   {
463 |    "cell_type": "markdown",
464 |    "metadata": {},
465 |    "source": [
466 |     "### plot graph between Age, Age_median , Age_random"
467 |    ]
468 |   },
469 |   {
470 |    "cell_type": "code",
471 |    "execution_count": 54,
472 |    "metadata": {},
473 |    "outputs": [
474 |     {
475 |      "data": {
476 |       "text/plain": [
477 |        "<matplotlib.legend.Legend at 0x2165179dca0>"
478 |       ]
479 |      },
480 |      "execution_count": 54,
481 |      "metadata": {},
482 |      "output_type": "execute_result"
483 |     },
484 |     {
485 |      "data": {
486 |       "image/png": 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\n",
487 |       "text/plain": [
488 |        "<Figure size 864x576 with 1 Axes>"
489 |       ]
490 |      },
491 |      "metadata": {
492 |       "needs_background": "light"
493 |      },
494 |      "output_type": "display_data"
495 |     }
496 |    ],
497 |    "source": [
498 |     "plt.figure(figsize=(12,8))\n",
499 |     "df.Age.plot(kind='kde',color='b')\n",
500 |     "df.Age_median.plot(kind='kde',color='y')\n",
501 |     "df.Age_random.plot(kind='kde',color='r')\n",
502 |     "plt.legend()"
503 |    ]
504 |   },
505 |   {
506 |    "cell_type": "markdown",
507 |    "metadata": {},
508 |    "source": [
509 |     "#### Note: \n",
510 |     "- from plot we can say that Random Sample Imputation work better than Mean Median Mode imputation is some cases.\n",
511 |     "- When we use Median to fillna there is Distortion\n",
512 |     "- There is Less distortion in random sample\n",
513 |     "- In every cases random sample imputation wont work"
514 |    ]
515 |   },
516 |   {
517 |    "cell_type": "markdown",
518 |    "metadata": {},
519 |    "source": [
520 |     "### Please drop a comment and share your feedback 👍🏻\n",
521 |     "## Thank you 😇😇 !"
522 |    ]
523 |   },
524 |   {
525 |    "cell_type": "code",
526 |    "execution_count": null,
527 |    "metadata": {},
528 |    "outputs": [],
529 |    "source": []
530 |   }
531 |  ],
532 |  "metadata": {
533 |   "kernelspec": {
534 |    "display_name": "Python 3",
535 |    "language": "python",
536 |    "name": "python3"
537 |   },
538 |   "language_info": {
539 |    "codemirror_mode": {
540 |     "name": "ipython",
541 |     "version": 3
542 |    },
543 |    "file_extension": ".py",
544 |    "mimetype": "text/x-python",
545 |    "name": "python",
546 |    "nbconvert_exporter": "python",
547 |    "pygments_lexer": "ipython3",
548 |    "version": "3.8.5"
549 |   }
550 |  },
551 |  "nbformat": 4,
552 |  "nbformat_minor": 4
553 | }
554 | 


--------------------------------------------------------------------------------
/Titanic.csv:
--------------------------------------------------------------------------------
  1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
  2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
  3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
  4 | 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
  5 | 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
  6 | 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
  7 | 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
  8 | 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
  9 | 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
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116 | 115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
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122 | 121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
123 | 122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
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130 | 129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
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132 | 131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
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147 | 146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
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153 | 152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
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157 | 156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
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160 | 159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
161 | 160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
162 | 161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
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165 | 164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
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171 | 170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
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175 | 174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
176 | 175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
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179 | 178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
180 | 179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
181 | 180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
182 | 181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
183 | 182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
184 | 183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
185 | 184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
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187 | 186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
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190 | 189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
191 | 190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
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199 | 198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
200 | 199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
201 | 200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
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203 | 202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
204 | 203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
205 | 204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
206 | 205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
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208 | 207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
209 | 208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
210 | 209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
211 | 210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
212 | 211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
213 | 212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
214 | 213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
215 | 214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
216 | 215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
217 | 216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
218 | 217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
219 | 218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
220 | 219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
221 | 220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
222 | 221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
223 | 222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
224 | 223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
225 | 224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
226 | 225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
227 | 226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
228 | 227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
229 | 228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
230 | 229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
231 | 230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
232 | 231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
233 | 232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
234 | 233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
235 | 234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
236 | 235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
237 | 236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
238 | 237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
239 | 238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
240 | 239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
241 | 240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
242 | 241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
243 | 242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
244 | 243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
245 | 244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
246 | 245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
247 | 246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
248 | 247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
249 | 248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
250 | 249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
251 | 250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
252 | 251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
253 | 252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
254 | 253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
255 | 254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
256 | 255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
257 | 256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
258 | 257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
259 | 258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
260 | 259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
261 | 260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
262 | 261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
263 | 262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
264 | 263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
265 | 264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
266 | 265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
267 | 266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
268 | 267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
269 | 268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
270 | 269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
271 | 270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
272 | 271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
273 | 272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
274 | 273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
275 | 274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
276 | 275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
277 | 276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
278 | 277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
279 | 278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
280 | 279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
281 | 280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
282 | 281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
283 | 282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
284 | 283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
285 | 284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
286 | 285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
287 | 286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
288 | 287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
289 | 288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
290 | 289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
291 | 290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
292 | 291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
293 | 292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
294 | 293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
295 | 294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
296 | 295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
297 | 296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
298 | 297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
299 | 298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
300 | 299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
301 | 300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
302 | 301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
303 | 302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
304 | 303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
305 | 304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
306 | 305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
307 | 306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
308 | 307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
309 | 308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
310 | 309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
311 | 310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
312 | 311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
313 | 312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
314 | 313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
315 | 314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
316 | 315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
317 | 316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
318 | 317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
319 | 318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
320 | 319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
321 | 320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
322 | 321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
323 | 322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
324 | 323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
325 | 324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
326 | 325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
327 | 326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
328 | 327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
329 | 328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
330 | 329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
331 | 330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
332 | 331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
333 | 332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
334 | 333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
335 | 334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
336 | 335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
337 | 336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
338 | 337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
339 | 338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
340 | 339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
341 | 340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
342 | 341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
343 | 342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
344 | 343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
345 | 344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
346 | 345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
347 | 346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
348 | 347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
349 | 348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
350 | 349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
351 | 350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
352 | 351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
353 | 352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
354 | 353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
355 | 354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
356 | 355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
357 | 356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
358 | 357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
359 | 358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
360 | 359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
361 | 360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
362 | 361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
363 | 362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
364 | 363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
365 | 364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
366 | 365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
367 | 366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
368 | 367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
369 | 368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
370 | 369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
371 | 370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
372 | 371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
373 | 372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
374 | 373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
375 | 374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
376 | 375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
377 | 376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
378 | 377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
379 | 378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
380 | 379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
381 | 380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
382 | 381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
383 | 382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
384 | 383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
385 | 384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
386 | 385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
387 | 386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
388 | 387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
389 | 388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
390 | 389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
391 | 390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
392 | 391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
393 | 392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
394 | 393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
395 | 394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
396 | 395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
397 | 396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
398 | 397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
399 | 398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
400 | 399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
401 | 400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
402 | 401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
403 | 402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
404 | 403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
405 | 404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
406 | 405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
407 | 406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
408 | 407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
409 | 408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
410 | 409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
411 | 410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
412 | 411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
413 | 412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
414 | 413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
415 | 414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
416 | 415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
417 | 416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
418 | 417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
419 | 418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
420 | 419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
421 | 420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
422 | 421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
423 | 422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
424 | 423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
425 | 424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
426 | 425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
427 | 426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
428 | 427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
429 | 428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
430 | 429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
431 | 430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
432 | 431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
433 | 432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
434 | 433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
435 | 434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
436 | 435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
437 | 436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
438 | 437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
439 | 438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
440 | 439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
441 | 440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
442 | 441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
443 | 442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
444 | 443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
445 | 444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
446 | 445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
447 | 446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
448 | 447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
449 | 448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
450 | 449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
451 | 450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
452 | 451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
453 | 452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
454 | 453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
455 | 454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
456 | 455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
457 | 456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
458 | 457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
459 | 458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
460 | 459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
461 | 460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
462 | 461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
463 | 462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
464 | 463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
465 | 464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
466 | 465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
467 | 466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
468 | 467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
469 | 468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
470 | 469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
471 | 470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
472 | 471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
473 | 472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
474 | 473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
475 | 474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
476 | 475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
477 | 476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
478 | 477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
479 | 478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
480 | 479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
481 | 480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
482 | 481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
483 | 482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
484 | 483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
485 | 484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
486 | 485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
487 | 486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
488 | 487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
489 | 488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
490 | 489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
491 | 490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
492 | 491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
493 | 492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
494 | 493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
495 | 494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
496 | 495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
497 | 496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
498 | 497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
499 | 498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
500 | 499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
501 | 500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
502 | 501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
503 | 502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
504 | 503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
505 | 504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
506 | 505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
507 | 506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
508 | 507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
509 | 508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
510 | 509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
511 | 510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
512 | 511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
513 | 512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
514 | 513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
515 | 514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
516 | 515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
517 | 516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
518 | 517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
519 | 518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
520 | 519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
521 | 520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
522 | 521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
523 | 522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
524 | 523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
525 | 524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
526 | 525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
527 | 526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
528 | 527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
529 | 528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
530 | 529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
531 | 530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
532 | 531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
533 | 532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
534 | 533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
535 | 534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
536 | 535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
537 | 536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
538 | 537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
539 | 538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
540 | 539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
541 | 540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
542 | 541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
543 | 542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
544 | 543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
545 | 544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
546 | 545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
547 | 546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
548 | 547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
549 | 548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
550 | 549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
551 | 550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
552 | 551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
553 | 552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
554 | 553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
555 | 554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
556 | 555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
557 | 556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
558 | 557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
559 | 558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
560 | 559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
561 | 560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
562 | 561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
563 | 562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
564 | 563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
565 | 564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
566 | 565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
567 | 566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
568 | 567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
569 | 568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
570 | 569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
571 | 570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
572 | 571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
573 | 572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
574 | 573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
575 | 574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
576 | 575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
577 | 576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
578 | 577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
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580 | 579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
581 | 580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
582 | 581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
583 | 582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
584 | 583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
585 | 584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
586 | 585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
587 | 586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
588 | 587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
589 | 588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
590 | 589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
591 | 590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
592 | 591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
593 | 592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
594 | 593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
595 | 594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
596 | 595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
597 | 596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
598 | 597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
599 | 598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
600 | 599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
601 | 600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
602 | 601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
603 | 602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
604 | 603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
605 | 604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
606 | 605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
607 | 606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
608 | 607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
609 | 608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
610 | 609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
611 | 610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
612 | 611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
613 | 612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
614 | 613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
615 | 614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
616 | 615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
617 | 616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
618 | 617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
619 | 618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
620 | 619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
621 | 620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
622 | 621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
623 | 622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
624 | 623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
625 | 624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
626 | 625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
627 | 626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
628 | 627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
629 | 628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
630 | 629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
631 | 630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
632 | 631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
633 | 632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
634 | 633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
635 | 634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
636 | 635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
637 | 636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
638 | 637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
639 | 638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
640 | 639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
641 | 640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
642 | 641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
643 | 642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
644 | 643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
645 | 644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
646 | 645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
647 | 646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
648 | 647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
649 | 648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
650 | 649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
651 | 650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
652 | 651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
653 | 652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
654 | 653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
655 | 654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
656 | 655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
657 | 656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
658 | 657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
659 | 658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
660 | 659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
661 | 660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
662 | 661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
663 | 662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
664 | 663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
665 | 664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
666 | 665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
667 | 666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
668 | 667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
669 | 668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
670 | 669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
671 | 670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
672 | 671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
673 | 672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
674 | 673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
675 | 674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
676 | 675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
677 | 676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
678 | 677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
679 | 678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
680 | 679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
681 | 680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
682 | 681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
683 | 682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
684 | 683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
685 | 684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
686 | 685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
687 | 686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
688 | 687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
689 | 688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
690 | 689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
691 | 690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
692 | 691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
693 | 692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
694 | 693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
695 | 694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
696 | 695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
697 | 696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
698 | 697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
699 | 698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
700 | 699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
701 | 700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
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703 | 702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
704 | 703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
705 | 704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
706 | 705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
707 | 706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
708 | 707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
709 | 708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
710 | 709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
711 | 710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
712 | 711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
713 | 712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
714 | 713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
715 | 714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
716 | 715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
717 | 716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
718 | 717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
719 | 718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
720 | 719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
721 | 720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
722 | 721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
723 | 722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
724 | 723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
725 | 724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
726 | 725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
727 | 726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
728 | 727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
729 | 728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
730 | 729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
731 | 730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
732 | 731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
733 | 732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
734 | 733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
735 | 734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
736 | 735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
737 | 736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
738 | 737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
739 | 738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
740 | 739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
741 | 740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
742 | 741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
743 | 742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
744 | 743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
745 | 744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
746 | 745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
747 | 746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
748 | 747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
749 | 748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
750 | 749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
751 | 750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
752 | 751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
753 | 752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
754 | 753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
755 | 754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
756 | 755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
757 | 756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
758 | 757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
759 | 758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
760 | 759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
761 | 760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
762 | 761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
763 | 762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
764 | 763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
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766 | 765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
767 | 766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
768 | 767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
769 | 768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
770 | 769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
771 | 770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
772 | 771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
773 | 772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
774 | 773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
775 | 774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
776 | 775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
777 | 776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
778 | 777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
779 | 778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
780 | 779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
781 | 780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
782 | 781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
783 | 782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
784 | 783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
785 | 784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
786 | 785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
787 | 786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
788 | 787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
789 | 788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
790 | 789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
791 | 790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
792 | 791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
793 | 792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
794 | 793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
795 | 794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
796 | 795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
797 | 796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
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799 | 798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
800 | 799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
801 | 800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
802 | 801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
803 | 802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
804 | 803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
805 | 804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
806 | 805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
807 | 806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
808 | 807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
809 | 808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
810 | 809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
811 | 810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
812 | 811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
813 | 812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
814 | 813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
815 | 814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
816 | 815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
817 | 816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
818 | 817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
819 | 818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
820 | 819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
821 | 820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
822 | 821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
823 | 822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
824 | 823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
825 | 824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
826 | 825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
827 | 826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
828 | 827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
829 | 828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
830 | 829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
831 | 830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
832 | 831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
833 | 832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
834 | 833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
835 | 834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
836 | 835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
837 | 836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
838 | 837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
839 | 838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
840 | 839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
841 | 840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
842 | 841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
843 | 842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
844 | 843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
845 | 844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
846 | 845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
847 | 846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
848 | 847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
849 | 848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
850 | 849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
851 | 850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
852 | 851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
853 | 852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
854 | 853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
855 | 854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
856 | 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
857 | 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
858 | 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
859 | 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
860 | 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
861 | 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
862 | 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
863 | 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
864 | 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
865 | 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
866 | 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
867 | 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
868 | 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
869 | 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
870 | 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
871 | 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
872 | 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
873 | 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
874 | 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
875 | 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
876 | 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
877 | 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
878 | 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
879 | 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
880 | 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
881 | 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
882 | 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
883 | 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
884 | 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
885 | 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
886 | 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
887 | 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
888 | 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
889 | 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
890 | 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
893 | 


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